Our approach is designed to increase confidence and reduce risk through demonstrating meaningful progress at every step.
“Mechanical Orchard’s team made quick work of dissecting our legacy systems, core business processes, and charted a course for our future technology.
Their disciplined approach to solving ‘the hard problems’ in software engineering is unparalleled in the industry, and delivered results from day one.”
We begin by studying the existing system to comprehend how it works. Together with the client, we select an application and zoom in on a single workload to start, usually the one that is hardest, riskiest, or both. This proves we can tackle the entire system. We learn how it behaves, operates, and relates to the rest of the current system, which forms the basis for creating behavioral tests of the workload.
Our AI tooling speeds up this process, from creating detailed dependency diagrams to monitoring how the system handles tasks. It allows us to complete our analysis in a few weeks instead of several months.
“Mechanical Orchard began by analyzing all the data flows through a single, critical system component, then precisely replicated its behavior in a cloud-based test environment with perfect fidelity.
That’s a powerful proof point.”
First, we establish a secure cloud environment connected to the legacy environment through a VPN. We then rewrite the workload in a modern language, guided by automated tests, then place it into the cloud environment, where it’s subjected to performance and functionality equivalence tests using actual data flows.
Once this workload is performing exactly as required, it moves into the production environment: the client can now decommission the legacy counterpart. Now, the newly-modernized workload is working in tandem with the legacy environment—with no disruption to the system overall.
We continue to rewrite the remaining workloads until the entire recreated system is confidently and securely running in the modern environment.
Because we only work with a single component at a time, each one has a proven fallback method. This profoundly limits the risk to your living, breathing system at any given time.
“It's impressive the level of knowledge that you all were able to build around what has always been hailed as almost impossible to understand, even by the people on my team. The command of the subject matter and the methodical approach that you brought to this is quite impressive.”
Moving forward, each subsequent workload deploys faster and faster, based on our tooling and what we’ve learned from prior workloads. Across both legacy and cloud environments, we continuously monitor and test the new behavior against live data running in real-time validating identical performance and function to the original and preventing any unexpected interruptions or surprises.
Once all workloads are deployed, the entire application can be turned off - and you have a fully functional, exact copy of this critical system running in the cloud.
“Mechanical Orchard’s team made quick work of dissecting our legacy systems, core business processes, and charted a course for our future technology.
Their disciplined approach to solving ‘the hard problems’ in software engineering is unparalleled in the industry, and delivered results from day one.”
We begin by studying the existing system to comprehend how it works. Together with the client, we select an application and zoom in on a single workload to start, usually the one that is hardest, riskiest, or both. This proves we can tackle the entire system. We learn how it behaves, operates, and relates to the rest of the current system, which forms the basis for creating behavioral tests of the workload.
Our AI tooling speeds up this process, from creating detailed dependency diagrams to monitoring how the system handles tasks. It allows us to complete our analysis in a few weeks instead of several months.
“Mechanical Orchard began by analyzing all the data flows through a single, critical system component, then precisely replicated its behavior in a cloud-based test environment with perfect fidelity.
That’s a powerful proof point.”
First, we establish a secure cloud environment connected to the legacy environment through a VPN. We then rewrite the workload in a modern language, guided by automated tests, then place it into the cloud environment, where it’s subjected to performance and functionality equivalence tests using actual data flows.
Once this workload is performing exactly as required, it moves into the production environment: the client can now decommission the legacy counterpart. Now, the newly-modernized workload is working in tandem with the legacy environment—with no disruption to the system overall.
We continue to rewrite the remaining workloads until the entire recreated system is confidently and securely running in the modern environment.
Because we only work with a single component at a time, each one has a proven fallback method. This profoundly limits the risk to your living, breathing system at any given time.
“It's impressive the level of knowledge that you all were able to build around what has always been hailed as almost impossible to understand, even by the people on my team. The command of the subject matter and the methodical approach that you brought to this is quite impressive.”
Moving forward, each subsequent workload deploys faster and faster, based on our tooling and what we’ve learned from prior workloads. Across both legacy and cloud environments, we continuously monitor and test the new behavior against live data running in real-time validating identical performance and function to the original and preventing any unexpected interruptions or surprises.
Once all workloads are deployed, the entire application can be turned off - and you have a fully functional, exact copy of this critical system running in the cloud.
We believe that every company deserves to realize their vision, free of constraints from the past. Our team's approach, learning aptitude and experience can help them move into this evolving version safely, reliably, fearlessly.
